row

Expression to identify the row or rows on which to perform the transform. Expression must evaluate to true or false.

Examples:

Expression

Description

Score >= 50

true if the value in the Score column is greater than 50.

LEN(LastName) > 8

true if the length of the value in the LastName column is greater than 8.

ISMISSING([Title])

true if the row value in the Title column is missing.

ISMISMATCHED(Score,['Integer'])

true if the row value in the Score column is mismatched against the Integer data type.

For the keep transform, if the expression for the row parameter evaluates to true for a row, it is kept in the dataset. Otherwise, it is removed.

Example:

keep row: (lastOrder >= 10000 && status == 'Active')

Output: Retains all rows in the dataset where the lastOrder value is greater than or equal to 10,000 and the customer status is Active.

Usage Notes:

Required?

Data Type

Yes

Expression that evaluates to true or false

Examples

Example - Remove old products and keep new orders

This examples illustrates how you can keep and delete rows from your dataset using the following transforms:

delete - Deletes a set of rows as evaluated by the conditional expression in the row parameter. See Delete Transform.

keep - Retains a set of rows as evaluated by the conditional expression in the row parameter. All other rows are deleted from the dataset. See Keep Transform.

Source:

Your dataset includes the following order information. You want to edit your dataset so that:

All orders for products that are no longer available are removed. These include the following product IDs: P100, P101, P102, P103.

All orders that were placed within the last 90 days are retained.

OrderId

OrderDate

ProdId

ProductName

ProductColor

Qty

OrderValue

1001

6/14/2015

P100

Hat

Brown

1

90

1002

1/15/2016

P101

Hat

Black

2

180

1003

11/11/2015

P103

Sweater

Black

3

255

1004

8/6/2015

P105

Cardigan

Red

4

320

1005

7/29/2015

P103

Sweeter

Black

5

375

1006

12/1/2015

P102

Pants

White

6

420

1007

12/28/2015

P107

T-shirt

White

7

390

1008

1/15/2016

P105

Cardigan

Red

8

420

1009

1/31/2016

P108

Coat

Navy

9

495

Transform:

First, you remove the orders for old products. Since the set of products is relatively small, you can start first by adding the following:

NOTE: Just preview this transform. Do not add it to your recipe yet.

delete row:(ProdId == 'P100')

When this step is previewed, you should notice that the top row in the above table is highlighted for removal. Notice how the transform relies on the ProdId value. If you look at the ProductName value, you might notice that there is a misspelling in one of the affected rows, so that column is not a good one for comparison purposes.

You can add the other product IDs to the transform in the following expansion of the transform, in which any row that has a matching ProdId value is removed: